The document provides biographical information about Prof. Dr. Son Vuong. It details his educational background, employment history, publications, conferences organized, and roles held. It also describes the Networks and Internet Computing Lab he directs at the University of British Columbia and some of the tools and systems developed within the lab, including BlueCT for interactive e-learning and BitVampire, the first P2P on-demand media streaming system.

1. Prof. Dr. Son Vuong
University of British Columbia
Vancouver, BC Canada
Email: vuong@cs.ubc.ca or stvuong@gmail.com
VGU
20/01/2016
Prof. Dr. Son Vuong
University of British Columbia
Vancouver, BC Canada
Email: vuong@cs.ubc.ca or stvuong@gmail.com
VGU
20/01/2016

2. Prof. Dr. Son Vuong’s Bio Sketch
 BSEE Cal State U, Sacto, MEng CarletonU, PhD, U. Waterloo
 Lecturer/Assistant Professor, U Waterloo, 1980-82
 Joined UBC/CS since 1982
 Director of Networks and Internet Computing Lab (NICLab)
 (Co)Author over 200 papers, supervise 80 MSc/PhD theses
 Co-edited three books, including “Recent Advances in Distributed
Multimedia Systems” published in 1999
 Co-Leader of $30M CAD GISST NCE Proposal (2000)
 (Co)chair and (Co)organizer of 15 international conferences (CCWC'17,
IEMCON’16, IEMCON’15, AMT’14, iThings’13NCAS’11, Multimedia’08,
DMS’08, NOMS’06, DMS'97, ICDCS'95, PSTV'94, FORTE'89, IWPTS'88).
 Consultant for the Canadian Government: Department of Communications
(DOC), Department of Industry (DOI)
 Board of Directors for companies, including Confederal Networks
(ConfedNet) and LIVES Mobile Corp.
 Professor Emeritus, UBC; Honorary VP, NTTU, Honorary Chair GWS
 BSEE Cal State U, Sacto, MEng CarletonU, PhD, U. Waterloo
 Lecturer/Assistant Professor, U Waterloo, 1980-82
 Joined UBC/CS since 1982
 Director of Networks and Internet Computing Lab (NICLab)
 (Co)Author over 200 papers, supervise 80 MSc/PhD theses
 Co-edited three books, including “Recent Advances in Distributed
Multimedia Systems” published in 1999
 Co-Leader of $30M CAD GISST NCE Proposal (2000)
 (Co)chair and (Co)organizer of 15 international conferences (CCWC'17,
IEMCON’16, IEMCON’15, AMT’14, iThings’13NCAS’11, Multimedia’08,
DMS’08, NOMS’06, DMS'97, ICDCS'95, PSTV'94, FORTE'89, IWPTS'88).
 Consultant for the Canadian Government: Department of Communications
(DOC), Department of Industry (DOI)
 Board of Directors for companies, including Confederal Networks
(ConfedNet) and LIVES Mobile Corp.
 Professor Emeritus, UBC; Honorary VP, NTTU, Honorary Chair GWS
2

3. The University of British Columbia
3

4. 4
The University of British Columbia
 108 years old in 2016
 A world class university with a spectacular location
 Consistently ranked among world’s top 35
universities
 #1 in CS in Canada, #16 in CS worldwide
#33 as global university (US News & World Report
on Education 2015)
 Annual budget of $1,600,000,000
 Over 50,000 students
 12 faculties and 11 schools, 2 campuses in Vancouver
and Kelowna
 108 years old in 2016
 A world class university with a spectacular location
 Consistently ranked among world’s top 35
universities
 #1 in CS in Canada, #16 in CS worldwide
#33 as global university (US News & World Report
on Education 2015)
 Annual budget of $1,600,000,000
 Over 50,000 students
 12 faculties and 11 schools, 2 campuses in Vancouver
and Kelowna

5. 5
The University of British Columbia
 World class faculties in medicine, life sciences, law,
computer science, engineering and management
 One home-grown and one resident Nobel Laureates:
Michael Smith, Nobel Prize in chemistry, 1993
Carl Wieman, Nobel Prize in physics, 2004
David Cheriton: UBC Faculty Alumni, Professor at
Stanford, a founder of Google. Donnation 2 million
USD to UBC, 25 million to Waterloo.
 World class faculties in medicine, life sciences, law,
computer science, engineering and management
 One home-grown and one resident Nobel Laureates:
Michael Smith, Nobel Prize in chemistry, 1993
Carl Wieman, Nobel Prize in physics, 2004
David Cheriton: UBC Faculty Alumni, Professor at
Stanford, a founder of Google. Donnation 2 million
USD to UBC, 25 million to Waterloo.

6. Tools/Systems Developed within NICLab
 BlueCTBlueCT: A Class Response System (“Clicker”) for
interactive e-learning via laptops and cell phones
 NEMO: Mobile Intelligent Agent System
 COOL-BitVampireBitVampire: The first P2P on-demand media
streaming system
 LePlazaLePlaza: A novel location-aware social network - Web-
based and via cell phones
 LIVESLIVES (Learning through Interactive Voice Educational
System): A voice-based mobile platform for e-learning.
Also LIVESMOBILE and LIVESGEO
 The GThe G--SystemSystem: uses the Internet of Things and social
networking for advanced society
 BlueCTBlueCT: A Class Response System (“Clicker”) for
interactive e-learning via laptops and cell phones
 NEMO: Mobile Intelligent Agent System
 COOL-BitVampireBitVampire: The first P2P on-demand media
streaming system
 LePlazaLePlaza: A novel location-aware social network - Web-
based and via cell phones
 LIVESLIVES (Learning through Interactive Voice Educational
System): A voice-based mobile platform for e-learning.
Also LIVESMOBILE and LIVESGEO
 The GThe G--SystemSystem: uses the Internet of Things and social
networking for advanced society
6

7. 7

8. 8

9. Sensor devices becoming widely available
- Programmable devices
- Off-the-shelf gadgets/tools
9

10. What does IOT Mean ?
Kevin Ashton coined "Internet of Things" phrase to
describe a system where the Internet is connected to
the physical world via ubiquitous sensors

11. How Ubiquitous?
Gartner: “IoT Installed Base Will Grow to 26 Billion Units By
2020.” That number might be too low.
 Every mobile
 Every auto
 Every door
 Every room
 Every sensor in
every device …
in every bed,
chair or bracelet
... in every
home, office,
building or
hospital room …
in every city and
village ... on
Earth ...
 Every part, on
every parts list
 Every sensor in
every device …
in every bed,
chair or bracelet
... in every
home, office,
building or
hospital room …
in every city and
village ... on
Earth ...

12. 12

13. More “Things” are being connected
Home/daily-life devices
Business and
Public infrastructure
Health-care
…
13

14. More “Things” are being connected
14
14
Image Courtesy: : CISCO

15. 15

16. 16

17. 17

18. 18

19. Silicon
Valley
10 km
19

20. 20

21. 21

22. 22

23. 23

24. 24

25. 25

26. 26

27. 27

28. Smart Shopping
28

29. Personal Computer Ring
29

30. Smart Trash Bucket
30

31. Prosthetic Eye with Camera
31

32. Sign Translation within Video
32

33. NEST Thermostat (3rd Gen)
Smart auto-adjustment and remote setting
33

34. A World of IoT …
5G5G
NetworkNetwork
Big DataBig Data
Body Area NetworkBody Area Network HealthcareHealthcare
5G5G
NetworkNetwork
M2MM2M

35. Observe
and
Sensing
Gather Learn,
Understand,
Hypothesize,
Optimize &
Predict
Change
IoT is a Four Part Journey
Networks
Sensors,
Monitors,
Devices,
Machines
Cloud, Big Data, Machine Learning,
Analytics and Decision Systems, AI
Machine
& People
Observe
and
Sensing
Analyze
Learn,
Understand,
Hypothesize,
Optimize &
Predict

36. Interactions among IoT components

37. The G-System
 The G-System applies The Internet of Things (IoT) beyond
the Smart City towards an Advanced Society.
 Aims at enhancing people’s quality of life and achieving a
harmonized society
 Has built-in Principle of Cause and Effect
 Collects visible and invisible information from the
environment (all Things)
 Dynamical update: G-Score (a diachronic metric)
 Detects opportunities from collected information and
hidden patterns behind the data
 Connects/disconnects people/G-Nodes and environment
in a “mysterious” (“karmic”) fashion; and suggests
actions/reactions
 The G-System applies The Internet of Things (IoT) beyond
the Smart City towards an Advanced Society.
 Aims at enhancing people’s quality of life and achieving a
harmonized society
 Has built-in Principle of Cause and Effect
 Collects visible and invisible information from the
environment (all Things)
 Dynamical update: G-Score (a diachronic metric)
 Detects opportunities from collected information and
hidden patterns behind the data
 Connects/disconnects people/G-Nodes and environment
in a “mysterious” (“karmic”) fashion; and suggests
actions/reactions

38. G-System vs Ecosystem
G-System
transformed
EcoSystem G-System
Green, Good, Global, Glory, God

39. G-Network - High Level Architecture
G gateway
G gateway
G-Network: towards LTE-Beyond (5G)
G gateway
3G base station
Wi-Fi hotspot
Internet
G gateway
G gateway
Cloud Computing
Internet

40. G-Infrastructure
Logical connection
Context-aware
P2P Overlay and
G Middleware
Context-aware
P2P Overlay and
G Middleware
Internet
Wifi
Sensor
RFID
Bluetooth
G-Node
(Node with G-Power)
Things – Passive Nodes
MANET
A network infrastructure of the G-System
Other Ad Hoc
Networks
Cloud
Computing
Context-aware
P2P Overlay and
G Middleware
Context-aware
P2P Overlay and
G Middleware
G-Node
(Node with G-Power)Nodes fully
connected and
grouped across the
networks by contexts
G-Node

41. G-Network Middleware System Architecture
P2P G-Network Middleware APIP2P G-Network Middleware API
G-Network Engine Run-time ModelG-Network Engine Run-time Model
P2P Service LayerP2P Service Layer
Topology-aware and Location-aware DHT-based Routing OverlayTopology-aware and Location-aware DHT-based Routing Overlay
Wi-Fi Direct AdapterWi-Fi Direct Adapter
DHT ServicesPartition Manager
P2P Service LayerP2P Service Layer
Semantic-based
Resource Manager
Quality of Services
Multi-hop
Routing

42. G-System Technical Challenges
 Explosion of data in G-Nodes tracking/monitoring
 Mobile AdHoc network (MANET) algorithms
 A context-aware epidemic routing algorithm supporting
various hardware, such as RFID, sensors
 A content distribution algorithm to exchange/share
content and contexts in a transparent fashion
 G-Middleware/Framework
 integrates a MANET and the Internet transparently
 Running services remotely through migratory agents
 G-System engine: rule-based, definition of G-Score, etc.
 Other challenges: security, fault tolerance, consistency.
 Explosion of data in G-Nodes tracking/monitoring
 Mobile AdHoc network (MANET) algorithms
 A context-aware epidemic routing algorithm supporting
various hardware, such as RFID, sensors
 A content distribution algorithm to exchange/share
content and contexts in a transparent fashion
 G-Middleware/Framework
 integrates a MANET and the Internet transparently
 Running services remotely through migratory agents
 G-System engine: rule-based, definition of G-Score, etc.
 Other challenges: security, fault tolerance, consistency.

43. Our Approach to the IoT
To approach this ultimate goal of ubiquitous
networking, we propose to develop the G-System
that integrates numerous novel methods,
paradigms and technologies including:
 The nextThe next--generation RFID (generation RFID (RFIDRFID--G2G2),),
 Mobile intelligent agents (NEMO, WISEMAN)
 Context (location)-aware social networking (LePlaza)
 P2P Interest management and computing (MOPAR)
 P2P adaptive video streaming (BitVampire)
 G-Services (LIVES for mobile learning)
 Security (access, privacy, botnet protection, etc.)
To approach this ultimate goal of ubiquitous
networking, we propose to develop the G-System
that integrates numerous novel methods,
paradigms and technologies including:
 The nextThe next--generation RFID (generation RFID (RFIDRFID--G2G2),),
 Mobile intelligent agents (NEMO, WISEMAN)
 Context (location)-aware social networking (LePlaza)
 P2P Interest management and computing (MOPAR)
 P2P adaptive video streaming (BitVampire)
 G-Services (LIVES for mobile learning)
 Security (access, privacy, botnet protection, etc.)
43

44. Functional Components of an RFID-G2 System
Dr. Min Chen: RA, UBC/CS; Professor, HUST
44

45. Randy H. Katz
UC Berkeley
IEMCON 2015: 6th International
Conference and Workshop on
Computing and Communication
Vancouver, Canada
15 October 2015

46. Greener Buildings
HumansHumans BuildingBuilding
Predictive
Controller
Predictive
Controller
1/20/2016 IEMCON 2015 46
EnvironmentEnvironment
Predictive
Controller
Predictions based on
Building Dynamics, Weather, Occupancy, Comfort

47. InstrumentationInstrumentation
ModelsModels
Plug LoadsPlug Loads
LightingLighting
FacilitiesFacilities
Building
Facility-
to-
Building
Facility-
to-
Building
Facility-
to-
Building
Facility-
to-
Building
Facility-
to-
Building
Facility-
to-
Building
Gen-to-
Building
Gen-to-
Building
Gen-
to-Grid
Gen-
to-Grid
uGrid-
to-Grid
uGrid-
to-Grid
Building-
to-Grid
Building-
to-Grid Building-
to-Grid
Building-
to-Grid
Energy Networks
47
InstrumentationInstrumentation
ModelsModels
ControlsControls
Building OSBuilding OS
FacilitiesFacilities
InstrumentationInstrumentation
ModelsModels
Routing/ControlRouting/Control
Grid OSGrid OS
Demand
Response
Demand
Response
Load FollowingLoad Following
Supply FollowingSupply Following
Grid
Facility-
to-
Building
Facility-
to-
Building
Facility-
to-
Building
Facility-
to-
Building
Instrumentatio
n
Instrumentatio
n
ModelsModels
ControlControl
Compressor
Scheduling
Compressor
Scheduling
Temperature
Maintenance
Temperature
Maintenance
Supply-Following
Loads
Storage-
to-
Building
Storage-
to-
Building
InstrumentationInstrumentation
ModelsModels
Power-Aware
Cluster Manager
Power-Aware
Cluster Manager
Load Balancer/
Scheduler
Load Balancer/
Scheduler
Web ServerWeb Server
Web App LogicWeb App Logic
DB/StorageDB/Storage
Machine Room
MR-to-
Building
MR-to-
Building
uGrid-
to-Grid
uGrid-
to-Grid

48. Software-Defined Building (SDB)
Planning
Visualization
Occupant
Satisfaction
Control /
Schedule
External
Energy Environment Outdoor EnvironmentPersonal Environment
48
BMS
Cyber Physical Building
Transport
Process
Loads
Occupant
Demand
Legacy
Instrumentation &
Control Interfaces
Pervasive
Sensing
Activity/Usage
Streams
Local Controllers
Occupant
Satisfaction
Multi-Objective
Model-Driven
Control
Building Operating System
and Service
HVAC
Electrical
Security,Fault,Anomaly
Detect&Management
Control /
Schedule
BIM proxydrvrs
Mapping
SoftZones
Physical Info Bus
privacy-pres. query
Physical
Models
Empirical
Models
App
sandbox
Light

49. Data
Broke
r
SDB a Kind of IoT Platform
Presentation and Analysis InterfacesPresentation and Analysis Interfaces
Analysis, Visualization
Data
Store
Data
Store
Event
Distribu-
tion
Discovery
Localiz-
ation
ControllersControllers
SchedulerScheduler
Subsampled,
Materialized
Data
Broke
r
Data
Broke
r
1/20/2016 IEMCON 2015 49
Sensors, Actuators, Event Streams
Data
Store
Data
Store
AdaptationAdaptation
Data
Store
Data
Store
Data
Store
Data
Store
Event
Distribu-
tion
Event
Distribu-
tion
DiscoveryDiscovery
Localiz-
ation
Localiz-
ation
ControllersControllers
Data, Metadata
Legacy, Archive

50. 1/20/2016 IEMCON 2015 50

51. Smart Buildings and the Cloud
Local
Controls
Local
Controls
Supervisory
Controls
Supervisory
Controls
ArchiveArchive
Learning
Algorithms
Learning
Algorithms
Spooled
Instrumentation
High-level
Controls
 “Unlimited” storage
and processing
 Global perspective,
integration across
instrumentation sources
 But latency, bandwidth,
connectivity issues
51
Local
Controls

52. Human-Centered Beyond
Internet-of-(Non-Human)Things
Processing/Analysis
Output
Uncertainty! I
n
O
u
t
52
Sensors Actuators
Input
Uncertainty!
Under
actuated!
The
Real
World
Humans
I
n
O
u
t

53. IOT: The Challenges
Every one of those sensors and control points is generating
data. Often, it's very informative and very private data.
Systems are needed to help those devices talk to each other,
manage all that data, and enforce proper access control.

54. Big Data means BIG Challenges
All of the messaging, management,
and access control technologies used
in these large-scale device networks
must be massively scalable.

55. Open Protocols
Current Internet and software methods are highly modular
(APIs), highly distributed (Cloud) and "loosely coupled"
(SOA). In today's systems, every LEGO brick comes from a
different source – and they all still must snap together.
This requires open, rapid and safeopen, rapid and safe development methods.
Current Internet and software methods are highly modular
(APIs), highly distributed (Cloud) and "loosely coupled"
(SOA). In today's systems, every LEGO brick comes from a
different source – and they all still must snap together.
This requires open, rapid and safeopen, rapid and safe development methods.

56. Open, Rapid and Safe:
Open Source and Open StandardsOpen Source and Open Standards
OPEN: Both work well. Easy to join, transparent to review.
FAST: Open source methods work well. Rapid iterations and
ease of contributions promote rapid development. (1)
SAFE: Open standards methods work well. Strong IPR rules,
balanced participation, neutral governance = usable work. (2)
OPEN: Both work well. Easy to join, transparent to review.
FAST: Open source methods work well. Rapid iterations and
ease of contributions promote rapid development. (1)
SAFE: Open standards methods work well. Strong IPR rules,
balanced participation, neutral governance = usable work. (2)

57. Key Challenges
for an Open Internet of Things
Lightweight protocols for
devices to work together,
communicate
Unique and extensible
identifiers for all those
billions of devices
Unique and extensible
identifiers for all those
billions of devices
Demand for API access and
interoperability
Cybersecurity
Privacy and Policy

58. 58

59. 59

60. Unintended Consequences
and Security Issues in IOT
 Shodan search engine
 TV camera is watching you
 Hackable home:
 Lamp (unit with no sec control)
 Web Camera
 Baby Monitor
 Fridge
 Car in garage
 Electronic pill reminder (Vitality Pill reminder)
 Shodan search engine
 TV camera is watching you
 Hackable home:
 Lamp (unit with no sec control)
 Web Camera
 Baby Monitor
 Fridge
 Car in garage
 Electronic pill reminder (Vitality Pill reminder)
60

61. 61

62. 62

63. 63

64. 64

65. 65

66. 66

67. 67

68. 68

69. 69

70. 70
(...)
 Sky Computing

71. 71

72. 72

73. 73

74. 74

75. The best way to predict the
future is to invest it

The best way to predict the
future is to invest it


76. Q/AQ/A
76